“Search” technologies (also known as utilities, tools, or engines) have become increasingly important over the years as more data are stored and transferred through information systems. One of the most popular search technologies is related to search engines for Internet servers, which service millions of information seekers daily. Search systems are incorporated in many data organizational systems, including, but not limited to, those associated with Intranets, archiving, cataloging, directory content, and categorical listings, among others.
Presently, a typical search system prompts for a query from an end-user, processes the query, and then generates a result set from either a source or a number of sources being searched. The result set that is then transmitted to and displayed on a computing device represents a limited subset of the whole query result set. The end-user typically must view results from several “pages” of results or scroll down a more extensive, tedious listing of results. Consequent end-user interactions with particular results have been limited and do not reflect real-time end-user awareness.
As search systems increase in size and complexity, queries tend to generate more results than the typical information seeker can effectively go through to find the best or desired results. Currently, a search result set for any given query is based on systems and methods that elect data into the resulting output set. However, a typical returned result set for a query contains “noise”, results that are not deemed relevant, significant, or content-laden by the end-user. There is a need to eliminate “noise” so information seekers (end-users) can effectively and efficiently go through search results.
Consequently, improvements for such systems and methods have focused primarily on index-based and/or query-based algorithms, functions, and organization, which influence how data are elected into the result set (i.e., size of result set), and/or data sorting operations, which influence the order of the results within a result set that has been returned to the end-user (i.e., weight or significance of particular result) that do not affect the size of the result set.
As for index-based approaches, these systems and methods, in recent years, have been manipulated by some entities to promote results that are typically not relevant, significant, or content-laden. For example, a classified search for “fish aquarium” may generate a result directed at a product completely unrelated to “fish” or “aquarium,” such as “gambling,” through malicious misrepresentation or false information. Other false entries may be attributed to data entry error. Some “search optimizing” technologies for website search engines take advantage of search algorithms that spider to find relevant content to create an index. These “optimizing” technologies distribute common keywords throughout a website or multiple websites to increase the chances of the search algorithm placing that website high in the result set, even though the website itself might not be content-laden. For example, some websites only contain more result sets rather than rich content pertaining to a subject matter in order to generate revenue associated with “pay-per-click” affiliations with popular Internet search engines.
As for query-based approaches, any particular query operation ends once an output result set has been given to the end-user. Some search systems contain methods that allow for a series of query prompts to refine search results thus producing a smaller subset from the previous result set; however, end-users are not able to perform operations on any particular result within the result set to exclude it from the given result set.
As for typical sorting operations, which allow the end-user some control to structure results, results are merely reorganized based on criteria pre-selected by the search system (such as “Most Recent”) devoid of any particular end-user's evaluations, such as tags or ratings. The results are displayed based solely on given criteria independent of the end-user's other interactions. Current sorting operations function independently of other interactive operations. While current sorting operations may be dependent on an interaction between the end-user and the result set, it does not ensure that a plurality of interactions will impact the current result set and subsequent queries for a particular end-user. For example, “sort” just sorts results; “rate” just places a value on a result the end-user assigns. Currently, these operations and others similar to these do not work conjointly to affect what is returned to a particular end-user in real-time.
Current search systems have little to no end-user awareness. In other words, current technological approaches lead search systems to generate the same result set to similarly made queries regardless of the particular end-user. The end-user has limited capabilities to control the structure of results in a real-time environment, and end-user interactions do not work conjointly to structure results for the end-user.
Current systems that have attempted to personalize the search environment to the end-user allow the end-user to store selected results on a computing device(s) and/or database(s). However, these search systems lack the awareness of the end-user's preferences and return redundant results to the end-user in subsequent queries. For example, in a search for a “house,” an end-user may save house listings they prefer. Subsequent searches may yield houses already “saved” by the end-user and overburden the said end-user who must peruse results that have been re-included and already processed by the said end-user. Current “save” operations do not loopback feedback to search operations. Rather, these operations only “recall” items. Furthermore, most search systems do not take into account a result the end-user wishes to eliminate in subsequent queries.
“Tagging” operations in current search systems, such as the system employed by Amazon.com of Seattle, Wash., work in a similar fashion to “saving” results. End-users may “tag” (categorize) an item using keywords and then proceed to search for items that have been tagged with similar keywords. An end-user may query a “tag” to find results personally tagged by said end-user or results “publicly” tagged by other end-users. These “tags” simply recall items associated with a keyword or keywords. In a “public” tag query, the end-user has little control over the result structure of a result set returned. If an end-user disagrees with a “tag,” he is not able to change the tag or that particular result from being re-returned to him in a similar query. Results returned to a “personal” tag query may be those assigned by the particular end-user; however, those results will not reflect other similarly tagged results from other end-users, thus the end-user does not gain the benefit from “social tagging”. The results to a “tag” query, similar to the “save” query, will only output a result that has been “tagged” (public and/or private) and will not incorporate new results unless they have been “tagged” as well from a previous query (usually from the main query system.) Amazon.com “tag” system employs a separate search system for “tagged” items, distinct from the main Amazon.com query system. End-users are prompted to search “Amazon.com” OR “Products Tagged With”. The feedback of end-user interactions through “tags” are not truly loop backed into the original search system. In other words, tag queries only work within a “tag” search, and the primary query system is generally unaware of results “tagged” by the end-user. Current “tagging” technologies do not work in concert with other end-user interactions to produce end-user awareness.
Current search systems that allow limited access to end-users to feedback information into the system (such as a prompt for end-user's rating of an item), do not allow such interaction to affect the results for the particular end-user. The given rating through various algorithms may affect the sorting and/or ranking of subsequent results based on those ratings; however, subsequent result sets for a particular end-user do not directly take into account the end-user's rating for the given result. For example, a particular end-user may rate an item as being very low in a given rating scale, while the majority of users have rated the item very high. That particular result for that item may appear highly ranked in subsequent result sets for the said end-user despite the said end-user's low evaluation for the result containing that particular item. Any end-user interaction currently affects search systems as a whole and produce similar results regardless of end-user.
Current rating operations that are tailored only to an end-user, such as the rating operation for RSS items in a query search for Newsgator.com of Denver, Colo., only highlight the number of stars an end-user designates. The end-user cannot proceed to change the rank of items. In other words, the result order cannot be changed or manipulated by the end-user in real-time nor is the interaction able to work in conjunction with other end-user interactions. Each item retains its order in the result set as it was originally returned. It is interesting to note that although Newsgator.com has the capability to delete items from general RSS streams (although this function is not present in the RSS query system), the deletion is not displayed in real-time. In other words, the page must be reloaded to reflect deletions.
Accordingly, there is a need for improved and more efficient search methods that facilitate end-user awareness, allowing end-users to structure results on particular preferences in regard to weight, significance, relevance, and/or other criteria to aid current and/or subsequent query results. Similarly, there is a need for a real-time system that allows a plurality of end-user interactions to be performed concurrently to achieve the best desired results for the particular end-user.